Robotic Quantification of Position Sense in Children With Perinatal Stroke
Bibliographic record
Abstract
Background Perinatal stroke is the leading cause of hemiparetic cerebral palsy. Motor deficits and their treatment are commonly emphasized in the literature. Sensory dysfunction may be an important contributor to disability, but it is difficult to measure accurately clinically. Objective Use robotics to quantify position sense deficits in hemiparetic children with perinatal stroke and determine their association with common clinical measures. Methods Case-control study. Participants were children aged 6 to 19 years with magnetic resonance imaging-confirmed unilateral perinatal arterial ischemic stroke or periventricular venous infarction and symptomatic hemiparetic cerebral palsy. Participants completed a position matching task using an exoskeleton robotic device (KINARM). Position matching variability, shift, and expansion/contraction area were measured with and without vision. Robotic outcomes were compared across stroke groups and controls and to clinical measures of disability (Assisting Hand Assessment) and sensory function. Results Forty stroke participants (22 arterial, 18 venous, median age 12 years, 43% female) were compared with 60 healthy controls. Position sense variability was impaired in arterial (6.01 ± 1.8 cm) and venous (5.42 ± 1.8 cm) stroke compared to controls (3.54 ± 0.9 cm, P < .001) with vision occluded. Impairment remained when vision was restored. Robotic measures correlated with functional disability. Sensitivity and specificity of clinical sensory tests were modest. Conclusions Robotic assessment of position sense is feasible in children with perinatal stroke. Impairment is common and worse in arterial lesions. Limited correction with vision suggests cortical sensory network dysfunction. Disordered position sense may represent a therapeutic target in hemiparetic cerebral palsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".